自己増殖型ニューラルネットワークを用いたノイズのある環境下での追加学習が可能な連想記憶システム Associative Memory System for Incremental Learning in Noisy Environment Using Self-Organizing Incremental Neural Network
We propose a novel associative memory that performs well on incremental learning and is robust to noisy data. Using the proposed method, new associative pairs presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment where the maximum number of associative pairs to be presented is unknown before learning. The proposed method deals with two types of noise. No conventional bidirectional associative memory deals with both types.
- 日本神経回路学会誌 = The Brain & neural networks
日本神経回路学会誌 = The Brain & neural networks 15(2), 98-109, 2008-06-05
Japanese Neural Network Society